Silver, Nate, 17, 238
Similarity, 178, 179
Similarity measures, 192, 197-200, 207
Simon, Herbert, 41, 225-226, 302
Simultaneous localization and mapping (SLAM), 166
Singularity, 28, 186, 286-289, 311
The Singularity Is Near (Kurzweil), 286
Siri, 37, 155, 161-162, 165, 172, 255
SKICAT (sky image cataloging and analysis tool), 15, 299
Skills, learners and, 8, 217-227
Skynet, 282-286
Sloan Digital Sky Survey, 15
Smith, Adam, 58
Snow, John, 183
Soar, chunking in, 226
Social networks, information propagation in, 231
The Society of Mind (Minsky), 35
Space complexity, 5
Spam filters, 23-24, 151-152, 168-169, 171
Sparse autoencoder, 117
Speech recognition, 155, 170-172, 276, 306
Speed, learning algorithms and, 139-142
Spin glasses, brain and, 102-103
Spinoza, Baruch, 58
Squared error, 241, 243
Stacked autoencoder, 117
Stacking, 238, 255, 309
States, value of, 219-221
Statistical algorithms, 8
Statistical learning, 37, 228, 297, 300, 307
Statistical modeling, 8. See also Machine learning
Statistical relational learning, 227-233, 254, 309
Statistical significance tests, 76-77
Statistics, Master Algorithm and, 31-32
Stock market predictions, neural networks and, 112, 302
Stream mining, 258
String theory, 46-47
Structure mapping, 199-200, 254, 307
Succession, rule of, 145-146
The Sun Also Rises (Hemingway), 106
Supervised learning, 209, 214, 220, 222, 226
Support vector machines (SVMs), 53, 179, 190-196, 240, 242, 244, 245, 254, 307
Support vectors, 191-193, 196, 243-244
Surfaces and Essences (Hofstadter & Sander), 200
Survival of the fittest programs, 131-134
Sutton, Rich, 221, 223
SVMs. See Support vector machines (SVMs)
Symbolists/symbolism, 51, 52, 54, 57-91
accuracy and, 75-79
Alchemy and, 251-252
analogizers vs., 200-202
assumptions and, 64
conjunctive concepts, 65-68
connectionists vs., 91, 94-95
decision tree induction, 85-89
further reading, 300-302
hill climbing and, 135
Hume and, 58-59
induction and, 80-83
intelligence and, 52, 89
inverse deduction and, 52, 82-85, 91
Master Algorithm and, 240-241, 242-243
nature and, 141
“no free lunch” theorem, 62-65
overfitting, 70-75
probability and, 173
problem of induction, 59-62
sets of rules, 68-70
Taleb, Nassim, 38, 158
Tamagotchi, 285
Technology
machine learning as, 236-237
sex and evolution of, 136-137
trends in, 21-22
Terrorists, data mining to catch, 232-233
Test set accuracy, 75-76, 78-79
Tetris, 32-33
Text classification, support vector machines and, 195-196
Thalamus, 27
Theory, defined, 46
Theory of cognition, 226
Theory of everything, Master Algorithm and, 46-48
Theory of intelligence, 35
Theory of problem solving, 225
Thinking, Fast and Slow (Kahneman), 141
Thorndike, Edward, 218
Through the Looking Glass (Carroll), 135
Tic-tac-toe, algorithm for, 3-4
Time, as principal component of memory, 217
Time complexity, 5
The Tipping Point (Gladwell), 105-106
Tolstoy, Leo, 66
Training set accuracy, 75-76, 79
Transistors, 1-2
Treaty banning robot warfare, 281
Truth, Bayesians and, 167
Turing, Alan, 34, 35, 286
Turing Award, 75, 156
Turing machine, 34, 250
Turing point, Singularity and, 286, 288
Turing test, 133-134
“Turning the Bayesian crank,” 149
UCI repository of data sets, 76
Uncertainty, 52, 90, 143-175
Unconstrained optimization, 193-194. See also Gradient descent
Underwood, Ben, 26, 299
Unemployment, machine learning and, 278-279
Unified inference algorithm, 256
United Nations, 281
US Patent and Trademark Office, 133
Universal learning algorithm. See Master Algorithm
Universal Turing machine, 34
Uplift modeling, 309
Valiant, Leslie, 75
Value of states, 219-221
Vapnik, Vladimir, 190, 192, 193, 195
Variance, 78-79
Variational inference, 164, 170
Venter, Craig, 289
Vinge, Vernor, 286
Virtual machines, 236
Visual cortex, 26
Viterbi algorithm, 165, 305
Voronoi diagrams, 181, 183
Wake-sleep algorithm, 103-104
Walmart, 11, 69-70
War, cyber-, 19-21, 279-282, 299, 310
War of the Worlds (radio program), 156
Watkins, Chris, 221, 223
Watson, James, 122, 236
Watson, Thomas J., Sr., 219
Watson (computer), 37, 42-43, 219, 237, 238
Wave equation, 30
Web 2.0, 21
Web advertising, 10-11, 160, 305
Weighted k-nearest-neighbor algorithm, 183-185, 190
Weights
attribute, 189
backpropagation and, 111
Master Algorithm and, 242
meta-learning and, 237-238
perceptron’s, 97-99
relational learning and, 229
of support vectors, 192-193
Welles, Orson, 156
Werbos, Paul, 113
Wigner, Eugene, 29
Will, George F., 276
Williams, Ronald, 112
Wilson, E. O., 31
Windows, 12, 133, 224
Wired (magazine), 265
Wizard of Oz problem, 285
Wolpert, David, 62, 238
Word of mouth, 231
Xbox Live, 160-161
XOR. See Exclusive-OR function (XOR)
Yahoo, 10
Yelp, 271, 277
YouTube, 266
Zuckerberg, Mark, 55
Pedro Domingos
PEDRO DOMINGOS is a professor of computer science at the University of Washington. He is a winner of the SIGKDD Innovation Award, the highest honor in data science. A fellow of the Association for the Advancement of Artificial Intelligence, he lives near Seattle.
